OpenRaiser/Intern-Atlas huggingface dataset:
Playable Video Generation
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github.io/playable-video-generation-website.
Teaching Turn-Taking Skills to Children with Autism using a Parrot-Like Robot
Venue: arXiv cs.RO 2021
Year: 2021

Robot Assisted Therapy is a new paradigm in many therapies such as the therapy of children with autism spectrum disorder. In this paper we present the use of a parrot-like robot as an assistive tool in turn taking therapy. The therapy is designed in the form of a card game between a child with autism and a therapist or the robot. The intervention was implemented in a single subject study format and the effect sizes for different turn taking variables are calculated. The results show that the child robot interaction had larger effect size than the child trainer effect size in most of the turn taking variables. Furthermore the therapist point of view on the proposed Robot Assisted Therapy is evaluated using a questionnaire. The therapist believes that the robot is appealing to children which may ease the therapy process. The therapist suggested to add other functionalities and games to let children with autism to learn more turn taking tasks and better generalize the learned tasks
GF-Flush: A GF(2) Algebraic Attack on Secure Scan Chains
Venue: arXiv cs.CR 2021
Year: 2021

Scan chains provide increased controllability and observability for testing digital circuits. The increased testability, however, can also be a source of information leakage for sensitive designs. The state-of-the-art defenses to secure scan chains apply dynamic keys to pseudo-randomly invert the scan vectors. In this paper, we pinpoint an algebraic vulnerability of these dynamic defenses that involves creating and solving a system of linear equations over the finite field GF(2). In particular, we propose a novel GF(2)-based flush attack that breaks even the most rigorous version of state-of-the-art dynamic defenses. Our experimental results demonstrate that our attack recovers the key as long as 500 bits in less than 7 seconds, the attack times are about one hundredth of state-of-the-art SAT based attacks on the same defenses. We then demonstrate how our attacks can be extended to scan chains compressed with Multiple-Input Signature Registers (MISRs).
Coalition Game Based Full-duplex Popular Content Distribution in mmWave Vehicular Networks
Venue: arXiv cs.IT 2021
Year: 2021

The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs) and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the number of received contents. The contribution of each member in the coalition can be transferable to its individual profit. While maximizing the number of received contents in the fixed time, the cooperative scheme also ensures the individual profit of each OBU in the coalition. We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks. Compared with other existing schemes, the proposed scheme has superior performances on the number of possessed contents and system fairness. Besides, the low complexity of the proposed algorithm is demonstrated by the switch operation number and CPU time.
A note on synthesizing geodesic based contact curves
Venue: arXiv cs.RO 2021
Year: 2021

The paper focuses on synthesizing optimal contact curves that can be used to ensure a rolling constraint between two bodies in relative motion. We show that geodesic based contact curves generated on both the contacting surfaces are sufficient conditions to ensure rolling. The differential geodesic equations, when modified, can ensure proper disturbance rejection in case the system of interacting bodies is perturbed from the desired curve. A corollary states that geodesic curves are generated on the surface if rolling constraints are satisfied. Simulations in the context of in-hand manipulations of the objects are used as examples.
Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub
Venue: arXiv cs.DB 2021
Year: 2021

Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of k nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution, namely DkM-SKS. Given m workers, DkM-SKS divides a set of subscriptions into m disjoint subsets based on a cost model so that each worker has almost the same kNN-update cost, to maintain load balancing. DkM-SKS allows an arbitrary approach to updating kNN of each subscription, so with a suitable in-memory index, DkM-SKS can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of DkM-SKS.
Effect of different patient peak arrivals on an Emergency Department via discrete event simulation
Venue: arXiv cs.CY 2021
Year: 2021

Emergency Departments (EDs) overcrowding is a well recognized worldwide phenomenon. The consequences range from long waiting times for visits and treatment of patients up to life-threatening health conditions. The international community is devoting greater and greater efforts to analyze this phenomenon aiming at reducing waiting times, improving the quality of the service. Within this framework, we propose a Discrete Event Simulation (DES) model to study the patient flows through a medium-size ED located in a region of Central Italy recently hit by a severe earthquake. In particular, our aim is to simulate unusual ED conditions, corresponding to critical events (like a natural disaster) that cause a sudden spike in the number of patient arrivals. The availability of detailed data concerning the ED processes enabled to build an accurate DES model and to perform extensive scenario analyses. The model provides a valid decision support system for the ED managers also in defining specific emergency plans to be activated in case of mass casualty disasters.
Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer. Moreover, since the dilated convolution extracts the multiscale features, we combine the dilated convolution with the channel attention block to enhance the feature representations. Due to the error that occurs from the difficulty of labeling crowds, especially for videos, imprecise or standard-inconsistent labels may lead to poor convergence for the model. To address this issue, we further propose a new patch-wise regression loss (PRL) to improve the original pixel-wise loss. Experimental results on three video-based benchmarks, i.e., the UCSD, Mall and WorldExpo'10 datasets, show that STDNet outperforms both image- and video-based state-of-the-art methods. The source codes are released at \url{https://github.com/STDNet/STDNet}.
The Mind's Eye: Visualizing Class-Agnostic Features of CNNs
Venue: arXiv cs.CV 2021
Year: 2021

Visual interpretability of Convolutional Neural Networks (CNNs) has gained significant popularity because of the great challenges that CNN complexity imposes to understanding their inner workings. Although many techniques have been proposed to visualize class features of CNNs, most of them do not provide a correspondence between inputs and the extracted features in specific layers. This prevents the discovery of stimuli that each layer responds better to. We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Exploring features in this class-agnostic manner allows for a greater focus on the feature extractor of CNNs. Our method uses a dual-objective activation maximization and distance minimization loss, without requiring a generator network nor modifications to the original model. This limits the number of FLOPs to that of the original network. We demonstrate the visualization quality on widely-used architectures.
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
Venue: arXiv cs.LG 2021
Year: 2021

Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes. Misspecified reward functions can lead to unintended negative side effects, and overall unsafe behavior. To overcome this problem, recent work proposed to augment the specified reward function with an impact regularizer that discourages behavior that has a big impact on the environment. Although initial results with impact regularizers seem promising in mitigating some types of side effects, important challenges remain. In this paper, we examine the main current challenges of impact regularizers and relate them to fundamental design decisions. We discuss in detail which challenges recent approaches address and which remain unsolved. Finally, we explore promising directions to overcome the unsolved challenges in preventing negative side effects with impact regularizers.
Playing games with Tito: Designing hybrid museum experiences for critical play
Venue: arXiv cs.HC 2021
Year: 2021

This article brings together two distinct, but related perspectives on playful museum experiences: Critical play and hybrid design. The article explores the challenges involved in combining these two perspectives, through the design of two hybrid museum experiences that aimed to facilitate critical play with/in the collections of the Museum of Yugoslavia and the highly contested heritage they represent. Based on reflections from the design process as well as feedback from test users we describe a series of challenges: Challenging the norms of visitor behaviour, challenging the role of the artefact, and challenging the curatorial authority. In conclusion we outline some possible design strategies to address these challenges.
Few-Shot Learning for Road Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Few-shot learning is a problem of high interest in the evolution of deep learning. In this work, we consider the problem of few-shot object detection (FSOD) in a real-world, class-imbalanced scenario. For our experiments, we utilize the India Driving Dataset (IDD), as it includes a class of less-occurring road objects in the image dataset and hence provides a setup suitable for few-shot learning. We evaluate both metric-learning and meta-learning based FSOD methods, in two experimental settings: (i) representative (same-domain) splits from IDD, that evaluates the ability of a model to learn in the context of road images, and (ii) object classes with less-occurring object samples, similar to the open-set setting in real-world. From our experiments, we demonstrate that the metric-learning method outperforms meta-learning on the novel classes by (i) 11.2 mAP points on the same domain, and (ii) 1.0 mAP point on the open-set. We also show that our extension of object classes in a real-world open dataset offers a rich ground for few-shot learning studies.
A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search
Venue: arXiv cs.IR 2021
Year: 2021

Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition. In the past decade, graph-based ANNS algorithms have been the leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, efficient solutions for retrieving the nearest neighbors for a given query. Nevertheless, these efforts focus on developing and optimizing algorithms with different approaches, so there is a real need for a comprehensive survey about the approaches' relative performance, strengths, and pitfalls. Thus here we provide a thorough comparative analysis and experimental evaluation of 13 representative graph-based ANNS algorithms via a new taxonomy and fine-grained pipeline. We compared each algorithm in a uniform test environment on eight real-world datasets and 12 synthetic datasets with varying sizes and characteristics. Our study yields novel discoveries, offerings several useful principles to improve algorithms, thus designing an optimized method that outperforms the state-of-the-art algorithms. This effort also helped us pinpoint algorithms' working portions, along with rule-of-thumb recommendations about promising research directions and suitable algorithms for practitioners in different fields.
Inductive Synthesis for Probabilistic Programs Reaches New Horizons
Venue: arXiv cs.LO 2021
Year: 2021

This paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a PCTL specification. The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family. These CEs leverage the semantics of the family in the form of bounds on its best- and worst-case behaviour provided by a deductive oracle using an MDP abstraction. The method further monitors the performance of the synthesis and adaptively switches between the inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to acceleration of the synthesis process on a wide range of benchmarks. For challenging problems, such as the synthesis of decentralized partially-observable controllers, we reduce the run-time from a day to minutes.
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we introduce the \textit{Layer-Peeled Model}, a nonconvex yet analytically tractable optimization program, in a quest to better understand deep neural networks that are trained for a sufficiently long time. As the name suggests, this new model is derived by isolating the topmost layer from the remainder of the neural network, followed by imposing certain constraints separately on the two parts of the network. We demonstrate that the Layer-Peeled Model, albeit simple, inherits many characteristics of well-trained neural networks, thereby offering an effective tool for explaining and predicting common empirical patterns of deep learning training. First, when working on class-balanced datasets, we prove that any solution to this model forms a simplex equiangular tight frame, which in part explains the recently discovered phenomenon of neural collapse \cite{papyan2020prevalence}. More importantly, when moving to the imbalanced case, our analysis of the Layer-Peeled Model reveals a hitherto unknown phenomenon that we term \textit{Minority Collapse}, which fundamentally limits the performance of deep learning models on the minority classes. In addition, we use the Layer-Peeled Model to gain insights into how to mitigate Minority Collapse. Interestingly, this phenomenon is first predicted by the Layer-Peeled Model before being confirmed by our computational experiments.
Predicting Nanorobot Shapes via Generative Models
Venue: arXiv cs.LG 2021
Year: 2021

The field of DNA nanotechnology has made it possible to assemble, with high yields, different structures that have actionable properties. For example, researchers have created components that can be actuated. An exciting next step is to combine these components into multifunctional nanorobots that could, potentially, perform complex tasks like swimming to a target location in the human body, detect an adverse reaction and then release a drug load to stop it. However, as we start to assemble more complex nanorobots, the yield of the desired nanorobot begins to decrease as the number of possible component combinations increases. Therefore, the ultimate goal of this work is to develop a predictive model to maximize yield. However, training predictive models typically requires a large dataset. For the nanorobots we are interested in assembling, this will be difficult to collect. This is because high-fidelity data, which allows us to characterize the shape and size of individual structures, is very time-consuming to collect, whereas low-fidelity data is readily available but only captures bulk statistics for different processes. Therefore, this work combines low- and high-fidelity data to train a generative model using a two-step process. We first use a relatively small, high-fidelity dataset to train a generative model. At run time, the model takes low-fidelity data and uses it to approximate the high-fidelity content. We do this by biasing the model towards samples with specific properties as measured by low-fidelity data. In this work we bias our distribution towards a desired node degree of a graphical model that we take as a surrogate representation of the nanorobots that this work will ultimately focus on. We have not yet accumulated a high-fidelity dataset of nanorobots, so we leverage the MolGAN architecture [1] and the QM9 small molecule dataset [2-3] to demonstrate our approach.
Gesture Recognition in Robotic Surgery: a Review
Venue: arXiv cs.CV 2021
Year: 2021

Objective: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. Methods: An article search was performed on 5 bibliographic databases with the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. Results: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. Conclusion: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. Significance: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.
Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure
Venue: arXiv cs.LG 2021
Year: 2021

We consider Markov Decision Processes (MDPs) in which every stationary policy induces the same graph structure for the underlying Markov chain and further, the graph has the following property: if we replace each recurrent class by a node, then the resulting graph is acyclic. For such MDPs, we prove the convergence of the stochastic dynamics associated with a version of optimistic policy iteration (OPI), suggested in Tsitsiklis (2002), in which the values associated with all the nodes visited during each iteration of the OPI are updated.
Are Work Zones and Connected Automated Vehicles Ready for a Harmonious Coexistence? A Scoping Review and Research Agenda
Venue: arXiv cs.CY 2021
Year: 2021

The recent advent of connected and automated vehicles (CAVs) is expected to transform the transportation system. CAV technologies are being developed rapidly and they are foreseen to penetrate the market at a rapid pace. On the other hand, work zones (WZs) have become common areas on highway systems as a result of the increasing construction and maintenance activities. The near future will therefore bring the coexistence of CAVs and WZs which makes their interaction inevitable. WZs expose all vehicles to a sudden and complex geometric change in the roadway environment, something that may challenge many of CAV navigation capabilities. WZs however also impose a space contraction resulting in adverse traffic impacts, something that legitimately calls for benefiting from the highly efficient CAV functions. CAVs should be able to reliably traverse WZ geometry and WZs should benefit from CAV intelligent functions. This paper reviews the state-of-the-art and the key concepts, opportunities, and challenges of deploying CAV systems at WZs. The reviewed subjects include traffic performance and behaviour, technologies and infrastructure, and regulatory considerations. Eighteen CAV mobility, safety, and environmental concepts and functions were distributed over the WZ area which was subdivided into five segments: further upstream, approach area, queuing area, WZ activity, and termination area. In addition, among other topics reviewed and discussed are detection of WZ features, smart traffic control devices, various technologies at connected WZs, cross-border harmonization, liability, insurance, and privacy. The paper also provides a research agenda with a list of research needs supported by experts rating and inputs. The paper aims to provide a bird eye view, but with necessary details that can benefit researchers, practitioners, and transportation agencies.